## EMA remaining useful life prediction with weighted bagging GPR algorithm

- Yujie Zhang, Datong Liu, Jinxiang Yu, Yu Peng, Xiyuan Peng
- Microelectronics Reliability
- 2017

1 Excerpt

- Published 2013 in Neural Computing and Applications

The lithium-ion battery cycle life prediction with particle filter (PF) depends on the physical or empirical model. However, in observation equation based on model, the adaptability and accuracy for individual battery under different operating conditions are not fully considered. Therefore, a novel fusion prognostic framework is proposed, in which the data-driven time series prediction model is adopted as observation equation, and combined to PF algorithm for lithium-ion battery cycle life prediction. Firstly, the nonlinear degradation feature of the lithium-ion battery capacity degradation is analyzed, and then, the nonlinear accelerated degradation factor is extracted to improve prediction ability of linear AR model. So an optimized nonlinear degradation autoregressive (ND–AR) time series model for remaining useful life (RUL) estimation of lithium-ion batteries is introduced. Then, the ND–AR model is used to realize multi-step prediction of the battery capacity degradation states. Finally, to improve the uncertainty representation ability of the standard PF algorithm, the regularized particle filter is applied to design a fusion RUL estimation framework of lithium-ion battery. Experimental results with the lithium-ion battery test data from NASA and CALCE (The Center for Advanced Life Cycle Engineering, the University of Maryland) show that the proposed fusion prognostic approach can effectively predict the battery RUL with more accurate forecasting result and uncertainty representation of probability density distribution (pdf).

Citations per Year

Averaging **9 citations** per year over the last 3 years.

@article{Liu2013LithiumionBR,
title={Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm},
author={Datong Liu and Yue Luo and Jie Liu and Yu Peng and Limeng Guo and Michael G. Pecht},
journal={Neural Computing and Applications},
year={2013},
volume={25},
pages={557-572}
}